Fusion of multimodal features acquired by custom-developed computer vision and electronic nose equipment for the detection of Huanglongbing at various symptomatic stages

Abstract

Citrus is widely loved for its rich nutritional value and unique flavor, and also occupies an important place in agriculture and the economy. The citrus industry has suffered severe losses in recent years due to the proliferation of citrus Huanglongbing (HLB). The transmission of HLB occurs via the Asian citrus psyllid insect vector and grafting practices, with no efficacious therapeutic intervention identified to date, aside from mitigating its dissemination through prompt identification and eradication of infected citrus trees. Detection of HLB is difficult due to its incubation period and the variety of symptoms at different stages of infection. Hence, there exists a pressing requirement for a detection methodology capable of integrating multi-level features of HLB to facilitate precise identification of the disease across various stages of infection. Most of the existing assays use single sensing, which leads to limitations and incompleteness in identifying specific markers induced by HLB. In this study, the effective configuration and complementarity of multimodal sensory information is achieved by establishing a fusion and complementary mechanism at the level of pre-processing and analyzing multisource information. The extraction of computer vision and electronic nose features of citrus leaves was realized using custom-developed portable detection devices. The performance of HLB detection was compared on different datasets obtained by multimodal feature fusion methods which include direct fusion method, stepwise fusion method and the improved Recursive Feature Elimination and Cross Validation (RFECV) feature selection method. The improved RFECV feature selection method uses the RFECV algorithm for each classification step in the delineated stepwise classification model and performs the feature set preference by cross-validation. The final improved RFECV feature selection method worked best for fusion of visual and olfactory features with an accuracy of 95.38% for HLB samples at various symptomatic stages, with 94.23% for early stage HLB and 94.12% for Zn Def. & HLB-positive samples. Multimodal feature fusion for feature acquisition proved to be superior to feature acquisition from a single sensing source, with enhanced fusion of HLB-induced feature sets at the visual and olfactory levels. It helps to improve the stability of the HLB measurement model to achieve the detection of HLB samples in complex environments. This method can provide generalized technical support for the application of multi-source sensing information and multimodal feature fusion methods in plant disease detection.

Plant phenotyping relevance

柑橘葉の症状を対象に、カスタム開発した画像処理・電子鼻装置とマルチモーダル特徴融合によるHLB検出法を開発・評価しており、植物病態の取得・推定が研究の中心である。

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